In time-series applications, data often becomes less useful as it gets older. If you don't need your historical data, you can delete it once it reaches a certain age. Timescale lets you set up automatic data retention policies to discard old data. You can also fine-tune data retention by manually dropping chunks.
Often, you want to keep summaries of your historical data, but you don't need the raw data. You can downsample your older data by combining data retention with continuous aggregates.
Timescale charges based on how much storage you use, so you don't need to choose and pay for a fixed storage size when you create a new service. This means that you also don't need to worry about scaling your disk size as your data grows. You are only charged for the storage space that you actually use. Make sure you use compression, a data retention policy, and tiered storage, to help you manage costs.
Timescale data retention works on chunks, not on rows. Deleting data
row-by-row, for example with the PostgreSQL
DELETE command, can be slow. But
dropping data by the chunk is faster, because it deletes an entire file from
disk. It doesn't need garbage collection and defragmentation.
Whether you use a policy or manually drop chunks, Timescale drops data by the chunk. It only drops chunks where all the data is within the specified time range.
For example, consider the setup where you have 3 chunks containing data:
- More than 36 hours old
- Between 12 and 36 hours old
- From the last 12 hours
You manually drop chunks older than 24 hours. Only the oldest chunk is deleted. The middle chunk is retained, because it contains some data newer than 24 hours. No individual rows are deleted from that chunk.
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